Apparatus for estimating position of target, robot system having the same, and method thereof

ABSTRACT

An apparatus for estimating a position of a target may include a particle generator to generate a plurality of particles on a map, a particle selector to calculate position accuracy of each of the plurality of particles, based on data sensed related to a position of a target, and select, as a reference particle, at least one of the plurality of particles, based on the position accuracy, and a position determining device to determine the position of the target, based on the reference particle.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims under 35 U.S.C. § 119(a) the benefit of KoreanPatent Application No. 10-2021-0024400, filed in the Korean IntellectualProperty Office on Feb. 23, 2021, the entire contents of which areincorporated herein by reference.

BACKGROUND (a) Technical Field

The present disclosure relates to an apparatus for estimating a positionof a target, a robot system including the same, and a method thereof.

(b) Description of the Related Art

Recently, as technology related to a robot has been developed, varioustypes of robots have been used in households or industrial fields. Therobots range from household robots that perform housework, such ascleaning while moving around the house, to industrial robots thatperform mechanical work in industrial sites for manufacturing.

In particular, the robots may perform work in place of a human beingwhile moving in or through several zones. It is important to recognizethe current position of movable robots. As such, various arrangementshave been utilized to estimate the position of a movable robot by usinga separate wireless device or comparing position information betweenseveral time points. However, since these arrangements require aseparate communication device and a complex computational algorithm, ahigh-cost robot system is required.

SUMMARY

An aspect of the present disclosure provides an apparatus for estimatinga position of a target, capable of performing a global positioningoperation of a robot by using only a sensor provided in the robot,instead of a separate device or a complex algorithm, thereby reducingthe manufacturing cost of the robot and efficiently performing theprocedure of estimating a position, a robot system including the same,and a method thereof.

The technical problems to be solved by the present inventive concept arenot limited to the aforementioned problems, and any other technicalproblems not mentioned herein will be clearly understood from thefollowing description by those skilled in the art to which the presentdisclosure pertains.

According to an aspect of the present disclosure, an apparatus forestimating a position of a target may include a particle generator togenerate a plurality of particles on a map, a particle selector tocalculate position accuracy of each of the plurality of particles, basedon data sensed related to the position of the target, and select, as areference particle, at least one of the plurality of particles, based onthe position accuracy, and a position determining device to determinethe position of the target, based on the reference particle.

According to an embodiment, the particle selector may select a particlehaving position accuracy, which is equal to or greater than a referencevalue, as the reference particle.

According to an embodiment, the particle selector may calculate theposition accuracy of each of the plurality of particles, by comparingthe map with the sensed data.

According to an embodiment, the particle selector may calculate theposition accuracy of each of the plurality of particles, by performingconvolution for a cost map obtained through distance-transformation forthe map and the sensed data.

According to an embodiment, the particle selector may re-distributeremaining particles other than the reference particle, around thereference particle.

According to an embodiment, the particle selector may re-distributeremaining particles other than the reference particle, based on theposition accuracy of the reference particle.

According to an embodiment, the particle selector may re-calculateposition accuracy of each of a plurality of reference particles aftermoving the target to a specific distance or more, when the plurality ofreference particles are provided, and re-select the reference particle,based on the re-calculated position accuracies of the referenceparticles.

According to an embodiment, the particle selector may iteratively selectthe reference particle until the reference particles are convergent toone reference particle.

According to an embodiment, the position determining device maydetermine, as the position of the target, a position of the onereference particle to which the reference particles are convergent.

According to an embodiment, the position determining device maydetermine a position of a reference particle, which has the highestposition accuracy, of a plurality of reference particles as the positionof the target, when the plurality of reference particles are provided.

According to another aspect of the present disclosure, a robot systemmay include a sensor to sense a distance between a robot and asurrounding object and a position estimating device to calculateposition accuracy of each of a plurality of particles generated on amap, based on sensed data by the sensor, select, as a referenceparticle, at least one of the plurality of particles, based on theposition accuracy, and estimate a position of a target, based on thereference particle.

According to an embodiment, the sensor may include a LIDAR to detect adistance between the robot and the surrounding object.

According to an embodiment, the robot system may further include adriver to move a robot and calculate a moving distance of the target,based on a value measured through a wheel encoder.

According to another aspect of the present disclosure, a method forestimating a position of a target may include generating, by a particlegenerator, a plurality of particles on a map; calculating, by a particleselector, position accuracy of each of the plurality of particles, basedon data sensed related to the position of the target; selecting, by theparticular selector, at least one of the plurality of particles as areference particle, based on the position accuracy; and determining, bya position determining device, the position of the target, based on thereference particle.

According to an embodiment, the selecting of the at least one of theplurality of particles as the reference particle may include selectingthe particle having position accuracy, which is equal to or greater thana reference value, as the reference particle.

According to an embodiment, the selecting of the at least one of theplurality of particles as the reference particle may include calculatingthe position accuracy of each of the plurality of particles, bycomparing the map with the sensed data.

According to an embodiment, the selecting of the at least one of theplurality of particles as the reference particle may include calculatingthe position accuracy of each of the plurality of particles, byperforming convolution for a cost map obtained throughdistance-transformation for the map and the sensed data.

According to an embodiment, the selecting of the at least one of theplurality of particles as the reference particle may includere-calculating position accuracy of each of a plurality of referenceparticles after moving the target to a specific distance or more, whenthe plurality of reference particles are provided, and re-selecting thereference particle, based on the re-calculated position accuracies ofthe reference particles.

According to an embodiment, the selecting of the at least one of theplurality of particles as the reference particle may include iterativelyselecting the reference particle until the reference particles areconvergent to one reference particle.

According to an embodiment, the determining of the position of thetarget may include datelining, as the position of the target, a positionof the one reference particle to which the reference particles areconvergent.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentdisclosure will be more apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings:

FIG. 1 is a block diagram illustrating the configuration of a robotsystem, according to an embodiment of the present disclosure;

FIG. 2 is a block diagram illustrating the configuration of an apparatusfor estimating a position of a target, according to an embodiment of thepresent disclosure;

FIG. 3 is a view illustrating an operation of generating a particle inan apparatus for estimating a position of a target, according to anembodiment of the present disclosure;

FIG. 4 is a view illustrating an operation of selecting a referenceparticle in an apparatus for estimating a position of a target,according to an embodiment of the present disclosure;

FIG. 5 is a view illustrating an operation of generating a position inan apparatus for estimating a position of a target, according to anembodiment of the present disclosure;

FIG. 6 is a block diagram illustrating the configuration of an apparatusfor estimating a position of a target, according to an embodiment of thepresent disclosure;

FIG. 7 is a flowchart illustrating a method for estimating a position ofa target, according to an embodiment of the present disclosure; and

FIG. 8 illustrates a computing system, according to an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. Throughout the specification, unless explicitly describedto the contrary, the word “comprise” and variations such as “comprises”or “comprising” will be understood to imply the inclusion of statedelements but not the exclusion of any other elements. In addition, theterms “unit”, “-er”, “-or”, and “module” described in the specificationmean units for processing at least one function and operation, and canbe implemented by hardware components or software components andcombinations thereof.

Further, the control logic of the present disclosure may be embodied asnon-transitory computer readable media on a computer readable mediumcontaining executable program instructions executed by a processor,controller or the like. Examples of computer readable media include, butare not limited to, ROM, RAM, compact disc (CD)-ROMs, magnetic tapes,floppy disks, flash drives, smart cards and optical data storagedevices. The computer readable medium can also be distributed in networkcoupled computer systems so that the computer readable media is storedand executed in a distributed fashion, e.g., by a telematics server or aController Area Network (CAN).

Hereinafter, some embodiments of the present disclosure will bedescribed in detail with reference to the exemplary drawings. In addingthe reference numerals to the components of each drawing, it should benoted that the identical or equivalent component is designated by theidentical numeral even when they are displayed on other drawings.Further, in describing the embodiment of the present disclosure, adetailed description of well-known features or functions will be ruledout in order not to unnecessarily obscure the gist of the presentdisclosure.

In addition, in the following description of components according to anembodiment of the present disclosure, the terms ‘first’, ‘second’, ‘A’,13′, ‘(a)’, and ‘(b)’ may be used. These terms are merely intended todistinguish one component from another component, and the terms do notlimit the nature, sequence or order of the constituent components. Inaddition, unless otherwise defined, all terms used herein, includingtechnical or scientific terms, have the same meanings as those generallyunderstood by those skilled in the art to which the present disclosurepertains. Such terms as those defined in a generally used dictionary areto be interpreted as having meanings equal to the contextual meanings inthe relevant field of art, and are not to be interpreted as having idealor excessively formal meanings unless clearly defined as having such inthe present application.

Hereinafter, embodiments of the present disclosure will be described indetail with reference to FIGS. 1 to 8.

FIG. 1 is a block diagram illustrating the configuration of a robotsystem, according to an embodiment of the present disclosure.

Referring to FIG. 1, according to an embodiment of the presentdisclosure, a robot system 100 may include a sensor 110, a driver 120,and a position estimating device 130.

The sensor 110 may sense terrain information around a robot. Forexample, the sensor 110 may sense a distance between the robot and asurrounding object. In detail, the sensor 110 may transmit a signal (forexample, an optical signal) to the surrounding object (for example, awall or an obstacle) and may detect a signal reflected from thesurrounding object, thereby measuring the distance. In addition, theinformation on the distance measured by the sensor 110 may betransmitted to the position estimating device 130. For example, thesensor 110 may include a LIDAR sensor.

The driver 120 may move a robot. For example, the driver 120 may movethe robot to a destination through a wheel. In this case, the driver 120may calculate the moving distance of the robot, based on a valuemeasured through a wheel encoder. In addition, information on the movingdistance calculated by the driver 120 may be transmitted to the positionestimating device 130.

The position estimating device 130 may generate a plurality of particlesthroughout an entire portion of a map, and may calculate the accuracy(position accuracy) of the position of each of a plurality of particles,based on data sensed by the sensor 110. In this case, the particle maybe placed at a candidate position of a target (for example, a robot). Inthis case, the position estimating device 130 may calculate the positionaccuracy of each particle, by matching the data (for example, distanceinformation) sensed by the sensor 110 with each particle.

In addition, the position estimating device 130 may select at least oneof the plurality of particles as a reference particle, based on theposition accuracy calculated with respect to each particle. In thiscase, the reference particle may be selected to a particle having theposition accuracy, which is equal to or higher than a preset referencevalue. In addition, remaining particles other than the referenceparticle may be re-distributed around the reference particle.

In addition, the position estimating device 130 may estimate theposition of the target, based on the selected reference particle. Forexample, the position estimating device 130 may determine a referenceparticle, which has the highest position accuracy, of the referenceparticle, or may determine the position of the target by iterativelyselecting the reference particle, until the reference particle isconvergent to one reference particle after the target is moved by thedriver 120. The details thereof will be described later in detail withreference to FIGS. 2 to 6.

FIG. 2 is a block diagram illustrating the configuration of an apparatus(position estimating apparatus) for estimating a position of a target,according to an embodiment of the present disclosure.

Referring to FIG. 2, according to an embodiment of the presentdisclosure, the position estimating apparatus 130 may include a particlegenerator 131, a particle selector 132, and a position determiningdevice 133. In this case, the position estimating apparatus 130 of FIG.2 may have the substantially components as those of the positionestimating apparatus 130 of FIG. 1.

The particle generator 131 may generate a plurality of particles on amap. In this case, each particle may be placed at a candidate positionof a target to be estimated in position on the map. Alternatively, theparticles may be uniformly arranged at regular distances on the map.

The particle selector 132 may calculate the position accuracy of eachparticle, based on data sensed related to the position of the target. Inthis case, the particle selector 132 may calculate the position accuracyof each particle, by comparing the map with the sensed data. Forexample, the particle selector 132 may calculate the position accuracyof each particle, by comparing a position of each particle on the mapwith sense information obtained from an external sensor (for example,the sensor 110 of FIG. 1).

In detail, the particle selector 132 may calculate the position accuracyof each particle by performing convolution for a cost map, which isobtained by distance-transforming the map stored in advance, and thesensed data. In this case, the cost map is a result obtained throughdistance-transformation for a map of a zone in which the target ispositioned. The cost map may be configured such that the value in thecost map becomes gradually decreased, as the target approaches a pointhaving a wall or an obstacle on the map, and gradually increased, as thetarget approaches a point having no wall or obstacle.

In addition, the particle selector 132 may select at least one of theplurality of particles as a reference particle, based on the calculatedposition accuracy. In this case, the reference particle may be selectedas a particle that is likely to correspond to a current position of thetarget (for example, the robot) with higher probability. For example,the particle selector 132 may select a particle having the positionaccuracy, which is equal to or greater than the reference value, as thereference particle.

The particle selector 132 may re-distribute remaining particles otherthan the reference particle, around the reference particle. For example,the particle selector 132 may re-distribute the remaining particlesother than the reference particle, based on the position accuracy of thereference particle. In this case, when the plurality of referenceparticles are provided, the particle selector 132 may differentlydistribute remaining particles depending on the position accuracies ofeach reference particle. For example, when the position accuracies ofthree reference particles are 90%, 80%, and 60%, the remaining particlesmay be distributed around three reference particles at the ratio of9:8:6.

In addition, the particle selector 132 may re-calculate positionaccuracy of each reference particle after moving the target to aspecific distance or more, when the plurality of reference particles areprovided. In this case, the particle selector 132 may re-select areference particle, based on the re-calculated position accuracy of thereference particle. As described above, the particle selector 132 mayiteratively the reference particle until the reference particles areconvergent to one reference particle.

The position determining device 133 may determine the position of thetarget, based on the reference particle. For example, the positiondetermining device 133 may determine the position of one referenceparticle, to which reference particles are convergent through theparticle selector 132, as the position of the target. Alternatively, theposition determining device 133 may determine the position of thereference particle having the highest position accuracy as the positionof the target, when the plurality of reference particles are provided.

According to an embodiment of the present disclosure, in the apparatus(position estimating apparatus) 130 for estimating the position, and therobot system 100 including the same, the global positioning operation ofthe robot may be performed by using only the sensor provided in therobot instead of the separate device or the complex algorithm, therebyreducing the manufacturing cost of the robot and efficiently performingthe procedure of estimating the position.

FIG. 3 is a view illustrating an operation of generating a particle in aposition estimating apparatus, according to an embodiment of the presentdisclosure.

Referring to subfigure (a) of FIG. 3, reference numeral “R” represents atarget (for example, a robot) to be position-estimated by the positionestimating apparatus 130, and reference numeral “L1” represents senseddata measured through the sensor (for example, a LIDAR sensor) 110. Inthis case, reference numeral “L1” represents a portion of the structure(wall) extracted from the sensed data measured by a LIDAR sensor.Alternatively, as illustrated in subfigure (a) of FIG. 3, the positionestimating apparatus 130 may set an arbitrary position on the map as aposition of the target “R”.

Alternatively, referring to subfigure (b) of FIG. 3, the positionestimating apparatus 130 may generate a plurality of particles “P” atarbitrary positions on the map. In this case, the position estimatingapparatus 130 may uniformly generate the particles throughout the wholearea of the map, as illustrated in subfigure (b) of FIG. 3, or maygenerate the particles in only candidate positions in which the target“R” is likely to be positioned with a higher probability on the map. Forexample, coordinates are assigned to each particle “P”.

FIG. 4 is a view illustrating an operation of generating a particle in aposition estimating apparatus, according to an embodiment of the presentdisclosure.

Referring to subfigure (a) of FIG. 4, according to an embodiment of thepresent disclosure, reference numeral “C” represents a cost map obtainedby distance-transforming the map by the position estimating apparatus130, reference sign “M” represents data (for example, a wall), orreference sign “L” represents data measured through a LIDAR sensor.Alternatively, as illustrated by shade in subfigure (a) of FIG. 4, aportion, which is closer to a wall or an obstacle, of the cost map “C”is displayed to be darker because the portion has a lower value, and aportion, which is farther away from a wall or an obstacle, of the costmap “C” is displayed to be brighter because the portion has a highervalue. Accordingly, the position estimating apparatus 130 may determinewhether a wall or an obstacle is present around a specific position,based on the cost map “C”.

As described above, according to an embodiment of the presentdisclosure, the position estimating apparatus 130 may performconvolution for the data of the cost map “C” generated throughdistance-transformation and the sensed data received from the LIDARsensor. Accordingly, the position accuracy may be calculated withrespect to the plurality of particles generated in subfigure (a) of FIG.3.

As illustrated in subfigure (b) of FIG. 4, four particles of theplurality of particles are calculated as reference particles “P1” to“P4” by the position estimating apparatus 130. In this case, thereference particles “P1” to “P4” may be selected as particles havingposition accuracies equal to or higher than a reference value (forexample, 80%).

Alternatively, as illustrated subfigure (b) of FIG. 4, remainingparticles may be re-distributed around the reference particles “P1” to“P4”. In this case, the remaining particles may be differentlydistributed depending on the position accuracy of each of the referenceparticles “P1” to “P4”. For example, it may be understood from subfigure(b) FIG. 4 that the remaining particles are distributed in order fromthe reference particle “P4” to “P1”, because the position accuracy isheightened toward the reference particle “P1” from the referenceparticle “P4”.

FIG. 5 is a view illustrating an operation of estimating a position of atarget in a position estimating apparatus, according to an embodiment ofthe present disclosure.

Referring to subfigure (a) of FIG. 5, reference numeral “R” representsthat the target illustrated in FIGS. 3 and 4 is moved right by aspecific distance, and reference numeral “L2” represents data measuredthrough a LIDAR sensor. Reference numeral “P1”′ represents the referenceparticle “P1” when the plurality of reference particles “P1” to “P4”illustrated in subfigure (b) of FIG. 4 are convergent to one referenceparticle “P1” by the position estimating apparatus 130 according to anembodiment of the present disclosure. In this case, as illustrated insubfigure (a) of FIG. 5, a reference particle is re-selected after thetarget “R” is moved right by the specific distance through theabove-described manner. In this case, the above-described procedure maybe iteratively performed until the reference particles are convergent toone reference particle. For example, the converging procedure of thereference particles may be performed through a particle swarmoptimization (PSO) algorithm.

Subfigure (b) of FIG. 5 illustrates that the position estimatingapparatus 130 determines the position of the reference particle “P1′”illustrated in subfigure (a) of FIG. 5 as a current position of a targetR′. In addition, it may be understood from subfigure (b) of FIG. 5 thateven sensed data “L” by the LIDAR sensor corresponds to the currentposition of the real target R′.

As described above, conventionally, a typical robot estimates a currentposition based on a position at a previous time “t−1”. Accordingly, whenthe current position is lost from the robot system due to re-booting orerrors, the position may not be continuously estimated. However,according to an embodiment of the present disclosure, the robot system100 including the position estimating apparatus 130 may effectivelyestimate the current position even if the robot loses the currentposition, such that the operation of a robot system is continuouslyperformed.

FIG. 6 is a flowchart illustrating a method for estimating a position ofa target, according to an embodiment of the present disclosure.

Referring to FIG. 6, according to an embodiment of the presentdisclosure, the position estimating apparatus 130 may generate aparticle on a map (S10). In this case, the specific number of particlesmay be uniformly generated throughout the whole area of the map or maybe generated around a candidate position. In addition, the positionestimating apparatus 130 may measure position accuracy may with respectto each of the generated particles (S20). In this case, the positionestimating apparatus 130 may measure the position accuracy by measuringthe matching between sensed data by the sensor 110 (for example, a LIDARsensor) with a position of each particle on the map.

In addition, the position estimating apparatus 130 may select areference particle of the generated particles, based on the positionaccuracy (S30). For example, the position estimating apparatus 130 mayselect a particle, which has position accuracy equal to or higher than areference value, as the reference particle or may select “n” number ofparticles, which have higher position accuracy when the particles arearranged in order of higher position accuracy, as reference particles.

In addition, remaining particles of the particles may be re-distributedaround the reference particle (S40). For example, when a plurality ofreference particles are provided, the remaining particles may bere-distributed differently depending on the position accuracies of thereference particles.

Next, the position estimating apparatus 130 determines the convergenceof the reference particle (S50). When the reference particle isconvergent to one reference particle, the position of the referenceparticle may be determined as being the position of the target (S60).Meanwhile, when the reference particles is not convergent, but aplurality of reference particles are calculated (“NO”), the target maybe moved by a specific distance or more (S70). In addition, as thetarget is moved, the generated particles may be moved together (S80). Inaddition, S20 to S50, which are described above, may be iterativelyperformed until the reference particles re convergent to one referenceparticle.

FIG. 7 is a flowchart illustrating a method for estimating a position ofa target, according to an embodiment of the present disclosure.

Hereinafter, according to an embodiment of the present disclosure, amethod for estimating a position of a target will be described in detailwith reference to FIG. 7. Hereinafter, it is assumed that the positionestimating apparatus 130 of FIG. 2 performs the process of FIG. 7. Inaddition, in the description made with reference to FIG. 7, it may beunderstood that operations described as being performed by the apparatusare controlled by a processor of the position estimating apparatus 130.

Referring to FIG. 7, according to an embodiment of the presentdisclosure, in the method for estimating the position, a plurality ofparticles may be generated on a map (S110). In this case, each particlemay be placed at a candidate position of a target to be estimated inposition on a map. Alternatively, the particles may be uniformly placedat regular distances on the map.

In addition, the processor may calculate the position accuracy of eachparticle, based on the sensed data related to the position of the target(S120). In this case, the processor may calculate the position accuracyof each particle, by comparing the map with the sensed data. Forexample, the processor may calculate the position accuracy of eachparticle, by comparing a position of each particle on the map with thesense information obtained from an external sensor (for example, thesensor 110 of FIG. 1) in S120. In detail, in S120 the processor maycalculate the position accuracy of each particle by performingconvolution for a cost map obtained by distance-transforming a mapstored in advance and the sensed data.

Next, at least one particle of the plurality of particles may beselected as a reference particle, based on the calculated positionaccuracy (S130). In this case, the reference particle may be selected asa particle that is likely to correspond to a current position of thetarget (for example, the robot) with higher probability. For example,the processor may select a particle having the position accuracy, whichis equal to or greater than the reference value, as the referenceparticle in S130.

In addition, the processor may re-distribute remaining particles otherthan the reference particle, around the reference particle in S130. Forexample, the processor may re-distribute remaining particles other thanthe reference particle, based on the position accuracy of the referenceparticle. For example, when a plurality of reference particles areprovided, the remaining particles may be re-distributed differentlydepending on the position accuracies of the reference particles.

In addition, the processor may re-calculate the position accuracy ofeach reference particle after moving the target to a specific distanceor more, when a plurality of reference particles are provided, in S120and S130. In this case, the processor may re-select a referenceparticle, based on the re-calculated position accuracy of the referenceparticle. As described above, the processor may iteratively select thereference particle until the reference particles is convergent to onereference particle.

In addition, the processor may determine the position of the target,based on the reference particle (S140). For example, the processor maydetermine the position of one reference particle, to which referenceparticles are convergent, as the position of the target. Alternatively,the processor may determine the position of a reference particle havingthe highest position accuracy as the position of the target, when aplurality of reference particles are provided.

As described above, according to an embodiment of the presentdisclosure, in the method for estimating the position, the globalpositioning operation of the robot may be performed by using only thesensor provided in the robot instead of the separate device or thecomplex algorithm, thereby reducing the manufacturing cost of the robotand efficiently performing the procedure of estimating the position,

FIG. 8 illustrates a computing system, according to an embodiment of thepresent disclosure.

Referring to FIG. 8, a computing system 1000 may include at least oneprocessor 1100, a memory 1300, a user interface input device 1400, auser interface output device 1500, a storage 1600, and a networkinterface 1700, which are connected with each other via a bus 1200.

The processor 1100 may be a central processing unit (CPU) or asemiconductor device for processing instructions stored in the memory1300 and/or the storage 1600. Each of the memory 1300 and the storage1600 may include various types of volatile or non-volatile storagemedia. For example, the memory 1300 may include a read only memory (ROM)and a random access memory (RAM).

Thus, the operations of the methods or algorithms described inconnection with the embodiments disclosed in the present disclosure maybe directly implemented with a hardware module, a software module, orthe combinations thereof, executed by the processor 1100. The softwaremodule may reside on a storage medium (i.e., the memory 1300 and/or thestorage 1600), such as a RAM, a flash memory, a ROM, an erasable andprogrammable ROM (EPROM), an electrically EPROM (EEPROM), a register, ahard disc, a removable disc, or a compact disc-ROM (CD-ROM).

The exemplary storage medium may be coupled to the processor 1100. Theprocessor 1100 may read out information from the storage medium and maywrite information in the storage medium. Alternatively, the storagemedium may be integrated with the processor 1100. The processor andstorage medium may reside in an application specific integrated circuit(ASIC). The ASIC may reside in a user terminal. Alternatively, theprocessor and storage medium may reside as separate components of theuser terminal.

As described above, according to an embodiment of the presentdisclosure, in the apparatus for estimating the position, the robotsystem including the same, and the method thereof, the globalpositioning operation of the robot may be performed by using only thesensor provided in the robot, instead of the separate device or thecomplex algorithm, thereby reducing the manufacturing cost of the robotand efficiently performing the procedure of estimating the position,

Besides, a variety of effects directly or indirectly understood throughthe present disclosure may be provided.

Hereinabove, although the present disclosure has been described withreference to exemplary embodiments and the accompanying drawings, thepresent disclosure is not limited thereto, but may be variously modifiedand altered by those skilled in the art to which the present disclosurepertains without departing from the spirit and scope of the presentdisclosure claimed in the following claims.

Therefore, the exemplary embodiments of the present disclosure areprovided to explain the spirit and scope of the present disclosure, butnot to limit them, so that the spirit and scope of the presentdisclosure is not limited by the embodiments. The scope of the presentdisclosure should be construed on the basis of the accompanying claims,and all the technical ideas within the scope equivalent to the claimsshould be included in the scope of the present disclosure.

What is claimed is:
 1. An apparatus for estimating a position of atarget, the apparatus comprising: a particle generator configured togenerate a plurality of particles on a map; a particle selectorconfigured to calculate position accuracy of each of the plurality ofparticles, based on data sensed related to the position of the target,and select, as a reference particle, at least one of the plurality ofparticles, based on the position accuracy; and a position determiningdevice configured to determine the position of the target, based on thereference particle.
 2. The apparatus of claim 1, wherein the particleselector selects a particle having position accuracy, which is equal toor greater than a reference value, of the particles as the referenceparticle.
 3. The apparatus of claim 1, wherein the particle selectorcalculates the position accuracy of each of the plurality of particles,by comparing the map with the sensed data.
 4. The apparatus of claim 1,wherein the particle selector calculates the position accuracy of eachof the plurality of particles, by performing convolution for a cost mapobtained through distance-transformation for the map and the senseddata.
 5. The apparatus of claim 1, wherein the particle selector isconfigured to re-distribute remaining particles other than the referenceparticle, around the reference particle.
 6. The apparatus of claim 5,wherein the particle selector re-distributes the remaining particlesother than the reference particle, based on the position accuracy of thereference particle.
 7. The apparatus of claim 1, wherein the particleselector re-calculates position accuracy of each of a plurality ofreference particles after moving the target to a specific distance ormore, when the plurality of reference particles are provided, andre-selects the reference particle, based on the re-calculated positionaccuracies of the reference particles.
 8. The apparatus of claim 7,wherein the particle selector iteratively selects the reference particleuntil the reference particles are convergent to one reference particle.9. The apparatus of claim 8, wherein the position determining devicedetermines, as the position of the target, a position of the onereference particle to which the reference particles are convergent. 10.The apparatus of claim 1, wherein the position determining devicedetermines a position of a reference particle, which has the highestposition accuracy, of a plurality of reference particles as the positionof the target, when the plurality of reference particles are provided.11. A robot system comprising: a sensor configured to sense a distancebetween a robot and a surrounding object; and a position estimatingdevice configured to: calculate position accuracy of each of a pluralityof particles generated on a map, based on sensed data by the sensor;select, as a reference particle, at least one of the plurality ofparticles, based on the position accuracy; and estimate a position of atarget, based on the reference particle.
 12. The robot system of claim11, wherein the sensor includes a LIDAR sensor to detect the distancebetween the robot and the surrounding object.
 13. The robot system ofclaim 11, further comprising: a driver to move the robot and calculate amoving distance of the target, based on a value measured through a wheelencoder.
 14. A method for estimating a position of a target, the methodcomprising: generating, by a particle generator, a plurality ofparticles on a map; calculating, by a particle selector, positionaccuracy of each of the plurality of particles, based on data sensedrelated to the position of the target; selecting, by the particularselector, at least one of the plurality of particles as a referenceparticle, based on the position accuracy; and determining, by a positiondetermining device, the position of the target, based on the referenceparticle.
 15. The method of claim 14, wherein the selecting of the atleast one of the plurality of particles as the reference particleincludes: selecting a particle having position accuracy, which is equalto or greater than a reference value, of the particles as the referenceparticle.
 16. The method of claim 14, wherein the selecting of the atleast one of the plurality of particles as the reference particleincludes: calculating the position accuracy of each of the plurality ofparticles, by comparing the map with the sensed data.
 17. The method ofclaim 14, wherein the selecting of the at least one of the plurality ofparticles as the reference particle includes: calculating the positionaccuracy of each of the plurality of particles, by performingconvolution for a cost map obtained through distance-transformation forthe map and the sensed data.
 18. The method of claim 14, wherein theselecting of the at least one of the plurality of particles as thereference particle includes: re-calculating position accuracy of each ofa plurality of reference particles after moving the target to a specificdistance or more, when the plurality of reference particles areprovided; and re-selecting the reference particle, based on there-calculated position accuracies of the reference particles.
 19. Themethod of claim 18, wherein the selecting of the at least one of theplurality of particles as the reference particle includes: iterativelyselecting the reference particle until the reference particles areconvergent to one reference particle.
 20. The method of claim 19,wherein the determining of the position of the target includes:determining, as the position of the target, a position of the onereference particle to which the reference particles are convergent.